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Control and study of a real wind turbine
Computers and Electrical Engineering 80 (2019) 106492
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Control and study of a real wind turbineR Abdeldjalil Dahbi a,c,∗, Abdellatif Reama b, Messaoud Hamouda c, Nasreddine Nait-Said d, Mohamed-Said Nait-Said d a
Unité de Recherche en Énergies Renouvelables en Milieu Saharien, URERMS, Centre de Développement des Énergies Renouvelables, CDER, 01000 Adrar, Algeria Department of Engineering System, Paris Est University, ESIEE, Paris, France c Laboratoire de Developpement Durable et Informatique, Université Ahmed Draia, Adrar, Algeria d Department of Electrical Engineering, University of Batna 2, LSP-IE’, 2000, Rue Chahid Med El Hadi Boukhlouf, 05000 Batna, Algeria b
a r t i c l e
i n f o
Article history: Received 10 January 2019 Revised 7 October 2019 Accepted 8 October 2019
Keywords: Wind energy conversion system (WECS) Maximum power point tracking (MPPT) Pitch angle control Artificial neural network (ANN) Supervisory control and data acquisition (SCADA)
a b s t r a c t This paper studies the real wind turbine installed in Kaberten (Adrar), in south western Algeria. It is based on Doubly Fed Induction Generator (DFIG) connected to the grid. The main objectives of this research is to create a good model which reflects the real behavior of the studied Wind Energy conversion System (WECS), the optimization of the captured wind power, and the improvement of its performance. In this context, various control strategies were applied. When the power is lower than its nominal value, two Maximum Power Point Tracking (MPPT) strategies were applied and compared. When the power is over or closer to the nominal value, the speed limit and pitch angle controls based on Artificial Neural Network (ANN) approach are applied. The simulation results were compared to the real data of the Supervisory Control and Data Acquisition (SCADA). They proved the validity of the model and the performance improvement. © 2019 Published by Elsevier Ltd.
1. Introduction Recently, due to the oil crisis and environment pollution, the exploitation of renewable energies knows a high interest and growth. The wind power energy has a particular place, especially in remote areas, where the supply of electricity from the grid is either not possible or very expensive. Algeria has a considerable wind potential; it greatly varies from a region to another due to diverse topographies and climates. Adrar’s region is among the windiest areas that present an excellent potential of wind energy. It is located in south western Algeria (about 1540 km from Algiers), [1–4]. In addition, the annual mean of wind speed in this area is over 6 m/s [3–7], which makes possible the exploitation of the wind turbine. Therefore, the first wind farm was installed in Kaberten (73 km north of Adrar). This wind farm has a capacity of 10 MW, with a surface of 30 hectares; it is constituted of 12 identical (0.85 MW) wind turbines that are connected to the grid via fully controlled converters, as it is shown in Figs. 1 and 2, [1,4,8]: The wind turbines installed in Kaberten are named Gamesa G52-850 kW; they have three-blades and a height of 55 m. This wind farm is equipped with Supervisory Control and Data Acquisition (SCADA) systems for the control system and R
This paper is for regular issues of CAEE. Reviews processed and recommended for publication to the Editor-in-Chief by Associate Editor Dr. Jia Hu. Corresponding author at: Unité de Recherche en Énergies Renouvelables en Milieu Saharien, URERMS, Centre de Développement des Énergies Renouvelables, CDER, 010 0 0 Adrar, Algeria. E-mail address: [email protected] (A. Dahbi). ∗
https://doi.org/10.1016/j.compeleceng.2019.106492 0045-7906/© 2019 Published by Elsevier Ltd.
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Fig. 1. Control scheme of the studied Wind Energy Conversion System (WECS).
Fig. 2. Wind farm in Kaberten, Adrar, Algeria [8].
data logging, [1,9,10]. These wind turbines operate at variable speed with a pitch angle control thanks to the back-to-back converters topology, [11]. Since the 12 wind turbines are identical, the modeling, analysis, and the control in this work are focused on only one wind turbine, but they are valid for all others. This paper presents a new study in this field. It is organized in four sections as follows: the first section is for the introduction. The second section presents how to provide the wind turbine model and its approximation. The third section is reserved to the Maximum Power Point Tracking (MPPT) controls and their comparison. Then, in the fourth section the analysis and the controls applied in the third and fourth region are given, with applying an approach based on the Artificial Neural Network (ANN) to control both MPPT and pitch angle in the same time. Finally, an investigation of the efficiency improvement, and a comparison between the simulation and the real wind turbine Gamesa G52-850 kW characteristics are presented, based on the real data of (SCADA). 2. Wind turbine model The wind turbine converts a part of the wind kinetic energy into mechanical energy in order to turn the rotor blades. The mechanical power Pm harvested from the wind directly depends on the blade length Rt , air density ρ , the power coefficient Cp, and the wind speed V1 . It may be written as [12]:
Pm =
1 C p (λ )ρ AV1 3 2
(1)
Where: Pm is the mechanical power [W], ρ is the air density [kg/m3 ], A is the air [m2 ], V1 is the wind speed [m/s], λ and Cp are without unit. The specific air density ρ can be written by Hélimax Énergie Inc. [13]:
ρ=
100P0 Rd (T + 273.15 )
(2)
Where: P0 is the atmospheric pressure [1013.25 mbar], Rd : the constant of perfect gas [287 J K−1 kg−1 ], T: temperature [K]. The tip-speed ratio ‘λ’ is given by Dahbi et al. [14]:
λ=
t Rt V1
(3)
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Fig. 3. Real efficiency (Ce ) and trail (Ct ) coefficients characteristics of G52 wind turbine.
Where: Ωt is the wind turbine rotor speed [rad/s], Rt is the blade length [m]. The gear box is used for adapting the mechanical power to the generator by increasing the output speed and decreasing the output torque as it is expressed below, [15]:
G=
g Tt = t Tg
(4)
Where: Ωt, Ωg are respectively the rotor speed in the wind turbine side and generator side [rad/s]; Tt, Tg are respectively the torque in the wind turbine side and generator side [N.m], G: is without unit. The wind turbine torque can be calculated from the wind turbine power Pt [W] as:
Tt = G.Tg =
Pt
(5)
t
The dynamic equation between the speed and torques is given by:
Tg − Tem − f g = J
d g dt
(6)
with
J=
Jt + Jg G2
(7)
f =
ft + fg G2
(8)
Where: Tem is the electromagnetic torque [N.m], J, Jt , Jg: Are respectively the total inertia, wind turbine inertia, generator inertia, [kg.m2 ], f, ft , fg: Are respectively total friction, wind turbine friction, generator friction, [N.m /(rad/s)], The aerodynamic efficiency of the wind turbine is expressed by the power coefficient; the evolution of this latter depends on the design of the wind turbine. The relationship between the power coefficient Cp (λ,β ), pitch angle (β ) in [degree (°)], and the tip speed ratio (λ) can be established by the following numerical approximation, [16]:
C p (λ, β ) = C1
C
2
λi
− C3 .β − C4 exp
C
− λ5 i
+ C6 .λ
(9)
Where λi itself is a function of λ and β
1
λi
=
1
λ + 0.08β
−
0.035 1 + β3
(10)
The coefficients C1 –C6 depend on the wind turbine design and its characteristics. The produced output electric power can be calculated directly using the efficiency coefficient defined by the following equation [17]:
Ce = ηmec ηgCP
(11)
Where: ηmec et ηg are respectively the mechanical and generator efficiencies. Cp and Ce are without unit. After many tests, the comparison between the real characteristics of Gamesa (G52) and the simulated characteristics of the efficiency coefficient are respectively presented in Figs. 3 and 4, [18].
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Fig. 4. Characteristics of the simulated efficiency coefficient.
Fig. 5. Operation regions of the wind turbine.
A. Result analysis According to Figs. 3 and 4, a very good accuracy is noticed between the real and simulated curve of Gamesa G-52, which proves the validity of the developed mathematical model to emulate the behavior of the real studied wind turbine. This allows applying different controls or tests to predict the power production and fault diagnostics, as well as improving the efficiency of the wind turbine with a lesser time and a lower cost. According to the captured wind power, there are four operation modes of the wind turbine, as it is shown in Fig. 5, [15]: When the wind speed is enough (generally 3 or 4 m/s), the blades start turning the rotor, 1st region, Fig. 5. If the wind power is lower than that nominal value, the wind turbine operates automatically on MPPT mode. So, the value of the pitch angle should be minimal and constant at its minimal value (β min = 0). Thus, Cp value becomes a function of λ and reaches its maximum value (Cp max = Cp _opt = 0.48) at the particular λ named λopt (= 8.89), 2nd region, Fig. 5. Hence, to maximize the wind energy, λ should be maintained at λopt , Fig. 6, [14]. In order to hold the power coefficient at its maximum value, the MPPT control must be applied.
3. MPPT controls MPPT control is applied to maximize the captured wind power when the available power is lower than the nominal generator power. In this case, it is interesting to greatly benefit from this power without any risk on the generator and the electric equipment for different values of the wind speed, [14]. In order to enhance the WECS efficiency, two MPPT controls were developed and compared: the first is MPPT without Speed Control; the second is MPPT with Speed Control.
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Fig. 6. Characteristics of power coefficient.
Fig. 7. The speed control scheme.
3.1. MPPT without speed control In MPPT strategy without speed control, the measure of wind speed is not needed. It is only based on the rotor speed measurement and the subsequent determination of the desired generator torque (or power). In this case, the generator torque is controlled so as to be equal to its reference value given by the following control law, [19]:
Tem−ref = Kopt .2mec
(12)
with
Kopt =
1 C pmax 1 .ρ .π .Rt5 3 2 λ3opt G
(13)
The open loop (OL) control of the MPPT can enhance the wind turbine efficiency. However, its dynamic is slow. In order to improve the system dynamic, the MPPT with speed control is developed.
3.2. MPPT with speed control In this strategy, the maximum power is reached by controlling the rotor speed using the speed controller in a closed loop (CL) in a way to be equal to the optimal speed. Thus, it controls the generated electrical power (and therefore the torque) according to the relation below, [20]:
Tem−re f = Cont . re f − mec
(14)
Where: Cont is a speed controller. The optimal speed of the turbine is corresponding to the optimal value of the specific speed, the maximum value of the power coefficient, and the maximal power; it is given by Dahbi and Hachemi [21]:
tur−re f =
λopt .V1 Rt
This control scheme is shown in Fig. 7, [14]: In order to test the model and choose the best MPPT strategy, the same wind profile shown in Fig. 8 is applied.
(15)
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Fig. 8. Wind profile.
A. Results of MPPT without speed control
Fig. 9. Wind turbine torque with its reference.
Fig. 10. Power coefficient.
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Fig. 11. Efficeincy coefficient.
Fig. 12. Wind turbine electric produced power.
B. Results of MPPT with speed control
Fig. 13. Rotor speed with its reference.
Fig. 14. Power coefficient.
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Fig. 15. Efficeincy coefficient.
Fig. 16. Wind turbine electric produced power.
C. Comparison between MPPT Results
The following figures present the comparison between MPPT with speed control and MPPT without speed control.
Fig. 17. Power coefficient comparaison.
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Fig. 18. Efficiency coefficient dynamics.
Fig. 19. Comparison between electric produced power.
Result analysis: In order to obtain the maximum power, the torque must be adapted with the wind speed variations. Fig. 9 shows that the generator torque follows very well its reference corresponding to the maximal values of power coefficient and efficiency coefficient, Figs. 10 and 11. Consequently, the maximal values of the captured power from the wind are achieved, Fig. 12. In the case of MPPT with speed control, Fig. 13 shows a good operation of the generator rotor speed on the optimal reference speed; consequently, the maximal power coefficient, efficiency coefficient and maximal captured power are reached, Figs. 14–16. In both MPPT controls the minimal value of the pitch angle is automatically generated by the ANN (β min = 0). It is also shown that although the wind speed variations, the power coefficient and the efficiency coefficient remain constant at their maximal values, Figs. 17 and 18. However, the dynamic response in the MPPT control with speed control (CL) is better, which involves harvesting much power in transitory time. Moreover, this makes the system quickly adapts with the wind speed variations, Fig. 18. Furthermore, Fig. 19, obviously shows that the output power obtained in the case of MPPT control without speed control (OL) is a bit lower than that obtained in MPPT with speed control (CL). For this reason, this latter is the one which is adopted and used in the rest of this work. After the analysis and the selection of the best (MPPT) control applied in the 2nd region, it is necessary to pass to the analysis of controls applied when the available power and speed are higher than the nominal generator values, [20]. 4. Speed and power limitation controls When the available power surpasses the nominal value, two control approaches should be applied in both regions 3rd and 4th region, respectively. This is to insure the production continuity on one hand, and to protect the electrical system on the other hand. These controls are: speed limitation control and power limitation control. When the wind speed is relatively high but a bit lower than the nominal value (the wind speed where the power reaches the nominal value), the rotor speed may reach damaging values because of the slowness of the blade dynamic. So, the control system implemented in the microprocessor quickly limits the rotor speed constant at its maximal value by imposing the corresponding electromagnetic reference torque, Eq. (6). In this case the rotor speed is higher than the synchronous speed, therefore, the hyper synchronous operation is reached, (region‘3’), Figs. 5 and 20. However, the pitch angle is maintained constant at its minimal
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Fig. 20. Control scheme of speed and power limit.
Fig. 21. Wind profile used for testing regions controls.
Fig. 22. Power coefficient.
value (β min = 0), and the power coefficient remains in its optimal value (Cp = Cp max = Cp_ opt = 0.48) according to the Eq. (16). In this operation mode, the power is produced from the stator and the rotor side. When the wind speed is higher than its nominal value, the power becomes considerable and it can reach values that damage the generator and electric equipment. So, a blade orientation system based on ANN called “pitch angle control” operates in order to limit the captured power at the nominal one. This is realized by increasing and changing the value of the pitch angle β generated by the ANN. This angle is translated by the blades moving angle using motors, thus the
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Fig. 23. Efficiency coefficient.
Fig. 24. Rotor speed and its reference.
Fig. 25. Rotor speed with limitation (W2) and without limitation (W1).
captured power by the blades decreases proportionally with β , because in this case the blades are not face to the wind directly, (region‘4’), Fig. 20, [20]. When the wind speed becomes more considerable (over 25 m/s), the protection system forces the wind turbine to stop its operation using the hydraulic brakes, this is for reasons of safety and protection of the equipment [22]. In order to optimize the produced power according to the available wind power, the developed approach was applied, [20]. 4.1. Design of MPPT-Power limitation controls using artificial neural network (ANN) As it is seen above that the power coefficient is a common factor between MPPT and pitch angle control; hence this approach is based on Eqs. (1) and (9), and the following deduced equation [20]:
Cp (λ, β ) =Cp opt (λ, β ) =
2 Pn
ρ
AV31
=
α V31
(16)
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Fig. 26. Variation of the pitch angle.
Fig. 27. Mechanical power with limitation (P2) and without limitation (P1).
So, the optimal value of Cp for different operation zones can be imposed on the system by generation the corresponding value of the pitch angle using the Artificial Neural Network (ANN), Fig. 20, [20]. In this approach a Multi-Layer Perceptron (MLP) network is used. It consists of several layers. The inputs are λ and Cp , the output layer generates the optimal value of β for different operation regions. In order to test and validate the control laws of the wind turbine in all regions, the wind profile shown in Fig. 21 is applied. 4.2. Result analysis Fig. 21 shows that the wind speed before (t = 7.3 s) is lower than that the nominal one (V1 = 11.2 m/s). So, the power is lower than its nominal value, the control system operates automatically in MPPT mode in order to maximize the extracted wind power. This is translated by the maximum values of the power coefficient and the efficiency coefficient, (Figs. 22 and 23). That is thanks to the developed approach and the good control of the rotor speed (Figs. 24 and 25). However, the pitch angle generated by the ANN is maintained constant at its minimal value (β min = 0, 2nd region, Fig. 26). In order to protect the system when the rotor speed reaches high values, (after t = 6.3 s), the control system limits this speed quickly to its maximal value, because the pitch angle dynamic is slow. This technique is to prevent the system damaging from over speed (3rd region), Figs. 24 and 25. In this studied wind turbine, the maximal speed of this latter is (30.8 rpm), knowing that the gear box ratio is (G = 61.74). So, the maximal rotor speed becomes (1902 rpm) in the generator side, Figs. 25 and 28, [18]. When the wind speed exceeds its nominal value, it is necessary to avoid the excess power in order to protect the system. Therefore, the intelligent ANN rapidly generates the corresponding pitch angle in a way to maintain the electrical produced power constant and equal to the nominal value of the generator (850 kW), (Figs. 26 and 27). Thus, the power coefficient and the efficiency coefficient (Figs. 22 and 23) are modified and decreased. At the same time the speed limit control remains operating, (4th region), (Figs. 24 and 25) [20]. 5. Performances comparison As it is shown in Figs. 3 and 4, a very good accuracy is noticed from the comparison between the real characteristic of Gamesa G52 and the simulated characteristic of the efficiency coefficient. This proves the validity of the mathematical
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Fig. 28. Real power curve of Gamesa G52, (Data sheet) [18].
Fig. 29. Simulated power curve of Gamesa G52 (with (CL) MPPT).
model to emulate the real behavior of the wind turbine (Gamesa G-52). After applying different controls in all regions, it is necessary to compare the efficiency of the simulated characteristics against that of the real one in order to detect if it is improved. Such a comparison is depicted in Figs. 28–30, between the real characteristic and the simulated characteristic of the power, [1,8,18,23]. 5.1. Results analysis of the WECS According to the real characteristics of Gamesa G-52, Figs. 3 and 28, the maximum value of the efficiency coefficient is (Cemax = 0.46), corresponding to the wind speed of (8 m/s) and to the power value (307 kW), which is confirmed in the simulated characteristics of the same wind turbine, Figs. 3, 4, 28, and 29. At the wind speed (9 m/s), the power equals to (435.3 kW) in Fig. 28, whereas in Fig. 29 for the same wind speed, the power is higher and equals (436.4 kW). This excess of power is justified by the holding of the efficiency coefficient at its maximum value by the MPPT control; while the efficiency coefficient of G52 at this speed decreases to (0.45), Fig. 3. Thanks to the applied good MPPT control, the nominal wind speed of the simulated Gamesa G52 becomes (11.2 m/s) instead of (16 m/s). For the same reason, the cut–in wind speed becomes
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Fig. 30. Real SCADA data of the studied wind turbine [8].
Fig. 31. Real SCADA data of Kaberten (P2) and simulated power curve (P1) of Gamesa G52.
(3 m/s) instead of (4 m/s); which adds another advantage of reaching the nominal power in lower wind speed relatively. In addition, this makes the possibility of installing this wind turbine even in lower windy areas, where the wind speed is lower than that indicated in the real characteristics of the wind turbine, (16 m/s), Figs. 28 and 29. Figs. 30 and 31 present the comparison between the real SCADA data of Gamesa-G52 installed in Kaberten (collected by the Algerian company of electrical energy production, [8]), and the same controlled and simulated wind turbine. A very good accuracy is noticed between the real and simulated curves of Gamesa G-52. However, the captured power by the controlled wind turbine (P2) is higher than the real captured power (P1), thanks to the developed and applied MPPTANN approach. This indicates that the efficiency of Gamesa G-52 has been improved. 6. Conclusion This work presents a deep study on the real wind turbine (Gamesa G52-850 kW) installed in Kaberten (Algeria). The wind energy conversion system was modeled and controlled on different operation modes, according to wind speed. At first, the mathematical model was tested and compared with the real characteristics of the wind turbine. Then, the produced wind
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power was optimized thanks to the applied approaches. In case of lower wind speed than its nominal value, it is necessary to maximize the captured wind power. Therefore, two Maximum Power Point Tracking control strategies were developed, discussed and compared in order to choose the best one. It was found that the strategy with speed control is the best. When the wind speed becomes closer to its nominal value, the rotor speed may reach damaging values, so the intelligent control system limits the rotor speed at its maximal value in order to protect the system from over speed, (3rd region). When the wind speed reaches or exceeds its nominal value, the power limitation control based on the intelligent Artificial Neural Network controller automatically generates the corresponding value of the pitch angle to limits the produced power constant at its nominal value (4th region). Such techniques guarantee the safety of the power production. All these control strategies were verified and tested in all operation regions. The validity of the mathematical model of the real studied wind turbine has been checked thanks to a good comparison between the simulated characteristics and the real ones. The efficiency improvement was also performed. The model can simulate the real behavior of Gamesa G52, which makes the possibility of making future studies, such as the prediction of the power production, the fault diagnostics, fault tolerant control of the wind turbine system and other tests of performance improvement. Because time is money, all these predictions should be perfectly achieved as soon as possible. Declaration of Competing Interest I declare that I have no significant competing financial, professional, or personal interests that might have influenced the performance or presentation of the work described in this manuscript. CRediT authorship contribution statement Abdeldjalil Dahbi: Conceptualization, Formal analysis, Data curation, Writing - original draft, Writing - review & editing. Abdellatif Reama: Writing - review & editing. Messaoud Hamouda: Formal analysis, Data curation. Nasreddine Nait-Said: Writing - review & editing. Mohamed-Said Nait-Said: Formal analysis. Acknowledgments The used data in this paper were provided by SKTM through the SKTM-CDER convention. The authors highly thank Mr. Laroui Rachid, head of Kaberten wind farm, as well as Mr. Boulekhrass, PDG of SKTM for their profitable collaboration. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.compeleceng. 2019.106492. References [1] Louassa S, Guerri O, Kaabeche A, Yassaa N. Effects of local ambient air temperatures on wind park performance: case of the Kaberten wind park. Energy Sources 2019;41:1556–7230 03 Oct. [2] Benmedjahed M, Maouedj R. 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[17] Guellai N, Benmedjahed M, Boudia SM, Benmansur A, Tabet Hellal AM. Evaluation du gisement éolien dans la région des hauts plateaux Algériens : (cas de Batna). In: Proceedings of the 2nd international conference on energy and sustainable development; 2013. [18] Polígono Industrial Agustinos, C/A s/n. GAMESA G52-850 kW; 2007. Report. [19] Aimani SE. Towards a practical identification of a DFIG based wind generator model for grid assessment. Energy Procedia 2012;14:1677–83. [20] Dahbi A, Nait-Said N, Nait-Said MS. .A novel combined MPPT-pitch angle control for wide range variable speed wind turbine based on neural network. Int J Hydrogen Energy 2016;41:9427–42. [21] Dahbi A, Hachemi M. Influence of the parameters variations on the power injected to the network by wind turbine using PMSG. Acta Electroteh 2013;54(1):31–44. [22] Wei C-C. Conceptual weather environmental forecasting system for identifying potential failure of under-construction structures during typhoons’. J Wind Eng Ind Aerodyn 2017;168:48–59. [23] Zhang Z-Y, Wang K-S. Wind turbine fault detection based on SCADA data analysis using ANN. Adv Manuf 2014;2:70–8. Abdeldjalil Dahbi was born in Ouargla, Algeria. He received his engineering in electromechanics from the University of Bordj Bou Arréridj, 2009, and his Ph.D. degree in electric control from the University of Batna, 2018. He is currently a researcher at URERMS. He had patents and he reviewed in journals. His main research interests are: renewable energies, power electronics, system controls. Abdellatif Reama was born in Safi, Morocco. He obtained his Ph.D. degree in electrical engineering from the National Polytechnic Institute of Toulouse (ENSEEIHT) in 1987, France. He is a professor at the Department of Systems Engineering, ESIEE-Paris. His research field covers several aspects of design and control, with the optimization and real-time systems. Messaoud Hamouda was born in Adrar, Algeria. He received his Ph.D. degree in Electrotechnic in 2007 from the University of Oran (USTO-MB), Algeria. He was the Director of URERMS Research Unit. He is also a Professor and the Director of the Sustainable Development Laboratory (SDL) at Adrar’s University. His current research interests include renewables energies, power electronics and sustainable development. Nasreddine Naït-Saïd was born in Batna, Algeria. He received the Engineer Diploma in Electronic from the National Polytechnic Algiers High School, in 1988. He received the Ph.D. degree in electrical engineering from the University of Batna in 2003, where he is currently a full professor. His research interests include the electric drives and diagnosis, artificial neural networks, and renewable energy. Mohamed-Said Naït-Saïd was born in Batna, Algeria. He received his Ph.D. degree in electrical engineering from the University of Batna, 1992. Currently he is a full professor at Batna’s University, where he headed a research laboratory (LSPIE), Master course of Control and Diagnosis, and scientific committee. His research interests include the electric machines and their control drives and diagnosis.
Contents lists available at ScienceDirect
Computers and Electrical Engineering journal homepage: www.elsevier.com/locate/compeleceng
Control and study of a real wind turbineR Abdeldjalil Dahbi a,c,∗, Abdellatif Reama b, Messaoud Hamouda c, Nasreddine Nait-Said d, Mohamed-Said Nait-Said d a
Unité de Recherche en Énergies Renouvelables en Milieu Saharien, URERMS, Centre de Développement des Énergies Renouvelables, CDER, 01000 Adrar, Algeria Department of Engineering System, Paris Est University, ESIEE, Paris, France c Laboratoire de Developpement Durable et Informatique, Université Ahmed Draia, Adrar, Algeria d Department of Electrical Engineering, University of Batna 2, LSP-IE’, 2000, Rue Chahid Med El Hadi Boukhlouf, 05000 Batna, Algeria b
a r t i c l e
i n f o
Article history: Received 10 January 2019 Revised 7 October 2019 Accepted 8 October 2019
Keywords: Wind energy conversion system (WECS) Maximum power point tracking (MPPT) Pitch angle control Artificial neural network (ANN) Supervisory control and data acquisition (SCADA)
a b s t r a c t This paper studies the real wind turbine installed in Kaberten (Adrar), in south western Algeria. It is based on Doubly Fed Induction Generator (DFIG) connected to the grid. The main objectives of this research is to create a good model which reflects the real behavior of the studied Wind Energy conversion System (WECS), the optimization of the captured wind power, and the improvement of its performance. In this context, various control strategies were applied. When the power is lower than its nominal value, two Maximum Power Point Tracking (MPPT) strategies were applied and compared. When the power is over or closer to the nominal value, the speed limit and pitch angle controls based on Artificial Neural Network (ANN) approach are applied. The simulation results were compared to the real data of the Supervisory Control and Data Acquisition (SCADA). They proved the validity of the model and the performance improvement. © 2019 Published by Elsevier Ltd.
1. Introduction Recently, due to the oil crisis and environment pollution, the exploitation of renewable energies knows a high interest and growth. The wind power energy has a particular place, especially in remote areas, where the supply of electricity from the grid is either not possible or very expensive. Algeria has a considerable wind potential; it greatly varies from a region to another due to diverse topographies and climates. Adrar’s region is among the windiest areas that present an excellent potential of wind energy. It is located in south western Algeria (about 1540 km from Algiers), [1–4]. In addition, the annual mean of wind speed in this area is over 6 m/s [3–7], which makes possible the exploitation of the wind turbine. Therefore, the first wind farm was installed in Kaberten (73 km north of Adrar). This wind farm has a capacity of 10 MW, with a surface of 30 hectares; it is constituted of 12 identical (0.85 MW) wind turbines that are connected to the grid via fully controlled converters, as it is shown in Figs. 1 and 2, [1,4,8]: The wind turbines installed in Kaberten are named Gamesa G52-850 kW; they have three-blades and a height of 55 m. This wind farm is equipped with Supervisory Control and Data Acquisition (SCADA) systems for the control system and R
This paper is for regular issues of CAEE. Reviews processed and recommended for publication to the Editor-in-Chief by Associate Editor Dr. Jia Hu. Corresponding author at: Unité de Recherche en Énergies Renouvelables en Milieu Saharien, URERMS, Centre de Développement des Énergies Renouvelables, CDER, 010 0 0 Adrar, Algeria. E-mail address: [email protected] (A. Dahbi). ∗
https://doi.org/10.1016/j.compeleceng.2019.106492 0045-7906/© 2019 Published by Elsevier Ltd.
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Fig. 1. Control scheme of the studied Wind Energy Conversion System (WECS).
Fig. 2. Wind farm in Kaberten, Adrar, Algeria [8].
data logging, [1,9,10]. These wind turbines operate at variable speed with a pitch angle control thanks to the back-to-back converters topology, [11]. Since the 12 wind turbines are identical, the modeling, analysis, and the control in this work are focused on only one wind turbine, but they are valid for all others. This paper presents a new study in this field. It is organized in four sections as follows: the first section is for the introduction. The second section presents how to provide the wind turbine model and its approximation. The third section is reserved to the Maximum Power Point Tracking (MPPT) controls and their comparison. Then, in the fourth section the analysis and the controls applied in the third and fourth region are given, with applying an approach based on the Artificial Neural Network (ANN) to control both MPPT and pitch angle in the same time. Finally, an investigation of the efficiency improvement, and a comparison between the simulation and the real wind turbine Gamesa G52-850 kW characteristics are presented, based on the real data of (SCADA). 2. Wind turbine model The wind turbine converts a part of the wind kinetic energy into mechanical energy in order to turn the rotor blades. The mechanical power Pm harvested from the wind directly depends on the blade length Rt , air density ρ , the power coefficient Cp, and the wind speed V1 . It may be written as [12]:
Pm =
1 C p (λ )ρ AV1 3 2
(1)
Where: Pm is the mechanical power [W], ρ is the air density [kg/m3 ], A is the air [m2 ], V1 is the wind speed [m/s], λ and Cp are without unit. The specific air density ρ can be written by Hélimax Énergie Inc. [13]:
ρ=
100P0 Rd (T + 273.15 )
(2)
Where: P0 is the atmospheric pressure [1013.25 mbar], Rd : the constant of perfect gas [287 J K−1 kg−1 ], T: temperature [K]. The tip-speed ratio ‘λ’ is given by Dahbi et al. [14]:
λ=
t Rt V1
(3)
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Fig. 3. Real efficiency (Ce ) and trail (Ct ) coefficients characteristics of G52 wind turbine.
Where: Ωt is the wind turbine rotor speed [rad/s], Rt is the blade length [m]. The gear box is used for adapting the mechanical power to the generator by increasing the output speed and decreasing the output torque as it is expressed below, [15]:
G=
g Tt = t Tg
(4)
Where: Ωt, Ωg are respectively the rotor speed in the wind turbine side and generator side [rad/s]; Tt, Tg are respectively the torque in the wind turbine side and generator side [N.m], G: is without unit. The wind turbine torque can be calculated from the wind turbine power Pt [W] as:
Tt = G.Tg =
Pt
(5)
t
The dynamic equation between the speed and torques is given by:
Tg − Tem − f g = J
d g dt
(6)
with
J=
Jt + Jg G2
(7)
f =
ft + fg G2
(8)
Where: Tem is the electromagnetic torque [N.m], J, Jt , Jg: Are respectively the total inertia, wind turbine inertia, generator inertia, [kg.m2 ], f, ft , fg: Are respectively total friction, wind turbine friction, generator friction, [N.m /(rad/s)], The aerodynamic efficiency of the wind turbine is expressed by the power coefficient; the evolution of this latter depends on the design of the wind turbine. The relationship between the power coefficient Cp (λ,β ), pitch angle (β ) in [degree (°)], and the tip speed ratio (λ) can be established by the following numerical approximation, [16]:
C p (λ, β ) = C1
C
2
λi
− C3 .β − C4 exp
C
− λ5 i
+ C6 .λ
(9)
Where λi itself is a function of λ and β
1
λi
=
1
λ + 0.08β
−
0.035 1 + β3
(10)
The coefficients C1 –C6 depend on the wind turbine design and its characteristics. The produced output electric power can be calculated directly using the efficiency coefficient defined by the following equation [17]:
Ce = ηmec ηgCP
(11)
Where: ηmec et ηg are respectively the mechanical and generator efficiencies. Cp and Ce are without unit. After many tests, the comparison between the real characteristics of Gamesa (G52) and the simulated characteristics of the efficiency coefficient are respectively presented in Figs. 3 and 4, [18].
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Fig. 4. Characteristics of the simulated efficiency coefficient.
Fig. 5. Operation regions of the wind turbine.
A. Result analysis According to Figs. 3 and 4, a very good accuracy is noticed between the real and simulated curve of Gamesa G-52, which proves the validity of the developed mathematical model to emulate the behavior of the real studied wind turbine. This allows applying different controls or tests to predict the power production and fault diagnostics, as well as improving the efficiency of the wind turbine with a lesser time and a lower cost. According to the captured wind power, there are four operation modes of the wind turbine, as it is shown in Fig. 5, [15]: When the wind speed is enough (generally 3 or 4 m/s), the blades start turning the rotor, 1st region, Fig. 5. If the wind power is lower than that nominal value, the wind turbine operates automatically on MPPT mode. So, the value of the pitch angle should be minimal and constant at its minimal value (β min = 0). Thus, Cp value becomes a function of λ and reaches its maximum value (Cp max = Cp _opt = 0.48) at the particular λ named λopt (= 8.89), 2nd region, Fig. 5. Hence, to maximize the wind energy, λ should be maintained at λopt , Fig. 6, [14]. In order to hold the power coefficient at its maximum value, the MPPT control must be applied.
3. MPPT controls MPPT control is applied to maximize the captured wind power when the available power is lower than the nominal generator power. In this case, it is interesting to greatly benefit from this power without any risk on the generator and the electric equipment for different values of the wind speed, [14]. In order to enhance the WECS efficiency, two MPPT controls were developed and compared: the first is MPPT without Speed Control; the second is MPPT with Speed Control.
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Fig. 6. Characteristics of power coefficient.
Fig. 7. The speed control scheme.
3.1. MPPT without speed control In MPPT strategy without speed control, the measure of wind speed is not needed. It is only based on the rotor speed measurement and the subsequent determination of the desired generator torque (or power). In this case, the generator torque is controlled so as to be equal to its reference value given by the following control law, [19]:
Tem−ref = Kopt .2mec
(12)
with
Kopt =
1 C pmax 1 .ρ .π .Rt5 3 2 λ3opt G
(13)
The open loop (OL) control of the MPPT can enhance the wind turbine efficiency. However, its dynamic is slow. In order to improve the system dynamic, the MPPT with speed control is developed.
3.2. MPPT with speed control In this strategy, the maximum power is reached by controlling the rotor speed using the speed controller in a closed loop (CL) in a way to be equal to the optimal speed. Thus, it controls the generated electrical power (and therefore the torque) according to the relation below, [20]:
Tem−re f = Cont . re f − mec
(14)
Where: Cont is a speed controller. The optimal speed of the turbine is corresponding to the optimal value of the specific speed, the maximum value of the power coefficient, and the maximal power; it is given by Dahbi and Hachemi [21]:
tur−re f =
λopt .V1 Rt
This control scheme is shown in Fig. 7, [14]: In order to test the model and choose the best MPPT strategy, the same wind profile shown in Fig. 8 is applied.
(15)
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Fig. 8. Wind profile.
A. Results of MPPT without speed control
Fig. 9. Wind turbine torque with its reference.
Fig. 10. Power coefficient.
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Fig. 11. Efficeincy coefficient.
Fig. 12. Wind turbine electric produced power.
B. Results of MPPT with speed control
Fig. 13. Rotor speed with its reference.
Fig. 14. Power coefficient.
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Fig. 15. Efficeincy coefficient.
Fig. 16. Wind turbine electric produced power.
C. Comparison between MPPT Results
The following figures present the comparison between MPPT with speed control and MPPT without speed control.
Fig. 17. Power coefficient comparaison.
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Fig. 18. Efficiency coefficient dynamics.
Fig. 19. Comparison between electric produced power.
Result analysis: In order to obtain the maximum power, the torque must be adapted with the wind speed variations. Fig. 9 shows that the generator torque follows very well its reference corresponding to the maximal values of power coefficient and efficiency coefficient, Figs. 10 and 11. Consequently, the maximal values of the captured power from the wind are achieved, Fig. 12. In the case of MPPT with speed control, Fig. 13 shows a good operation of the generator rotor speed on the optimal reference speed; consequently, the maximal power coefficient, efficiency coefficient and maximal captured power are reached, Figs. 14–16. In both MPPT controls the minimal value of the pitch angle is automatically generated by the ANN (β min = 0). It is also shown that although the wind speed variations, the power coefficient and the efficiency coefficient remain constant at their maximal values, Figs. 17 and 18. However, the dynamic response in the MPPT control with speed control (CL) is better, which involves harvesting much power in transitory time. Moreover, this makes the system quickly adapts with the wind speed variations, Fig. 18. Furthermore, Fig. 19, obviously shows that the output power obtained in the case of MPPT control without speed control (OL) is a bit lower than that obtained in MPPT with speed control (CL). For this reason, this latter is the one which is adopted and used in the rest of this work. After the analysis and the selection of the best (MPPT) control applied in the 2nd region, it is necessary to pass to the analysis of controls applied when the available power and speed are higher than the nominal generator values, [20]. 4. Speed and power limitation controls When the available power surpasses the nominal value, two control approaches should be applied in both regions 3rd and 4th region, respectively. This is to insure the production continuity on one hand, and to protect the electrical system on the other hand. These controls are: speed limitation control and power limitation control. When the wind speed is relatively high but a bit lower than the nominal value (the wind speed where the power reaches the nominal value), the rotor speed may reach damaging values because of the slowness of the blade dynamic. So, the control system implemented in the microprocessor quickly limits the rotor speed constant at its maximal value by imposing the corresponding electromagnetic reference torque, Eq. (6). In this case the rotor speed is higher than the synchronous speed, therefore, the hyper synchronous operation is reached, (region‘3’), Figs. 5 and 20. However, the pitch angle is maintained constant at its minimal
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Fig. 20. Control scheme of speed and power limit.
Fig. 21. Wind profile used for testing regions controls.
Fig. 22. Power coefficient.
value (β min = 0), and the power coefficient remains in its optimal value (Cp = Cp max = Cp_ opt = 0.48) according to the Eq. (16). In this operation mode, the power is produced from the stator and the rotor side. When the wind speed is higher than its nominal value, the power becomes considerable and it can reach values that damage the generator and electric equipment. So, a blade orientation system based on ANN called “pitch angle control” operates in order to limit the captured power at the nominal one. This is realized by increasing and changing the value of the pitch angle β generated by the ANN. This angle is translated by the blades moving angle using motors, thus the
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Fig. 23. Efficiency coefficient.
Fig. 24. Rotor speed and its reference.
Fig. 25. Rotor speed with limitation (W2) and without limitation (W1).
captured power by the blades decreases proportionally with β , because in this case the blades are not face to the wind directly, (region‘4’), Fig. 20, [20]. When the wind speed becomes more considerable (over 25 m/s), the protection system forces the wind turbine to stop its operation using the hydraulic brakes, this is for reasons of safety and protection of the equipment [22]. In order to optimize the produced power according to the available wind power, the developed approach was applied, [20]. 4.1. Design of MPPT-Power limitation controls using artificial neural network (ANN) As it is seen above that the power coefficient is a common factor between MPPT and pitch angle control; hence this approach is based on Eqs. (1) and (9), and the following deduced equation [20]:
Cp (λ, β ) =Cp opt (λ, β ) =
2 Pn
ρ
AV31
=
α V31
(16)
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Fig. 26. Variation of the pitch angle.
Fig. 27. Mechanical power with limitation (P2) and without limitation (P1).
So, the optimal value of Cp for different operation zones can be imposed on the system by generation the corresponding value of the pitch angle using the Artificial Neural Network (ANN), Fig. 20, [20]. In this approach a Multi-Layer Perceptron (MLP) network is used. It consists of several layers. The inputs are λ and Cp , the output layer generates the optimal value of β for different operation regions. In order to test and validate the control laws of the wind turbine in all regions, the wind profile shown in Fig. 21 is applied. 4.2. Result analysis Fig. 21 shows that the wind speed before (t = 7.3 s) is lower than that the nominal one (V1 = 11.2 m/s). So, the power is lower than its nominal value, the control system operates automatically in MPPT mode in order to maximize the extracted wind power. This is translated by the maximum values of the power coefficient and the efficiency coefficient, (Figs. 22 and 23). That is thanks to the developed approach and the good control of the rotor speed (Figs. 24 and 25). However, the pitch angle generated by the ANN is maintained constant at its minimal value (β min = 0, 2nd region, Fig. 26). In order to protect the system when the rotor speed reaches high values, (after t = 6.3 s), the control system limits this speed quickly to its maximal value, because the pitch angle dynamic is slow. This technique is to prevent the system damaging from over speed (3rd region), Figs. 24 and 25. In this studied wind turbine, the maximal speed of this latter is (30.8 rpm), knowing that the gear box ratio is (G = 61.74). So, the maximal rotor speed becomes (1902 rpm) in the generator side, Figs. 25 and 28, [18]. When the wind speed exceeds its nominal value, it is necessary to avoid the excess power in order to protect the system. Therefore, the intelligent ANN rapidly generates the corresponding pitch angle in a way to maintain the electrical produced power constant and equal to the nominal value of the generator (850 kW), (Figs. 26 and 27). Thus, the power coefficient and the efficiency coefficient (Figs. 22 and 23) are modified and decreased. At the same time the speed limit control remains operating, (4th region), (Figs. 24 and 25) [20]. 5. Performances comparison As it is shown in Figs. 3 and 4, a very good accuracy is noticed from the comparison between the real characteristic of Gamesa G52 and the simulated characteristic of the efficiency coefficient. This proves the validity of the mathematical
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Fig. 28. Real power curve of Gamesa G52, (Data sheet) [18].
Fig. 29. Simulated power curve of Gamesa G52 (with (CL) MPPT).
model to emulate the real behavior of the wind turbine (Gamesa G-52). After applying different controls in all regions, it is necessary to compare the efficiency of the simulated characteristics against that of the real one in order to detect if it is improved. Such a comparison is depicted in Figs. 28–30, between the real characteristic and the simulated characteristic of the power, [1,8,18,23]. 5.1. Results analysis of the WECS According to the real characteristics of Gamesa G-52, Figs. 3 and 28, the maximum value of the efficiency coefficient is (Cemax = 0.46), corresponding to the wind speed of (8 m/s) and to the power value (307 kW), which is confirmed in the simulated characteristics of the same wind turbine, Figs. 3, 4, 28, and 29. At the wind speed (9 m/s), the power equals to (435.3 kW) in Fig. 28, whereas in Fig. 29 for the same wind speed, the power is higher and equals (436.4 kW). This excess of power is justified by the holding of the efficiency coefficient at its maximum value by the MPPT control; while the efficiency coefficient of G52 at this speed decreases to (0.45), Fig. 3. Thanks to the applied good MPPT control, the nominal wind speed of the simulated Gamesa G52 becomes (11.2 m/s) instead of (16 m/s). For the same reason, the cut–in wind speed becomes
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Fig. 30. Real SCADA data of the studied wind turbine [8].
Fig. 31. Real SCADA data of Kaberten (P2) and simulated power curve (P1) of Gamesa G52.
(3 m/s) instead of (4 m/s); which adds another advantage of reaching the nominal power in lower wind speed relatively. In addition, this makes the possibility of installing this wind turbine even in lower windy areas, where the wind speed is lower than that indicated in the real characteristics of the wind turbine, (16 m/s), Figs. 28 and 29. Figs. 30 and 31 present the comparison between the real SCADA data of Gamesa-G52 installed in Kaberten (collected by the Algerian company of electrical energy production, [8]), and the same controlled and simulated wind turbine. A very good accuracy is noticed between the real and simulated curves of Gamesa G-52. However, the captured power by the controlled wind turbine (P2) is higher than the real captured power (P1), thanks to the developed and applied MPPTANN approach. This indicates that the efficiency of Gamesa G-52 has been improved. 6. Conclusion This work presents a deep study on the real wind turbine (Gamesa G52-850 kW) installed in Kaberten (Algeria). The wind energy conversion system was modeled and controlled on different operation modes, according to wind speed. At first, the mathematical model was tested and compared with the real characteristics of the wind turbine. Then, the produced wind
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power was optimized thanks to the applied approaches. In case of lower wind speed than its nominal value, it is necessary to maximize the captured wind power. Therefore, two Maximum Power Point Tracking control strategies were developed, discussed and compared in order to choose the best one. It was found that the strategy with speed control is the best. When the wind speed becomes closer to its nominal value, the rotor speed may reach damaging values, so the intelligent control system limits the rotor speed at its maximal value in order to protect the system from over speed, (3rd region). When the wind speed reaches or exceeds its nominal value, the power limitation control based on the intelligent Artificial Neural Network controller automatically generates the corresponding value of the pitch angle to limits the produced power constant at its nominal value (4th region). Such techniques guarantee the safety of the power production. All these control strategies were verified and tested in all operation regions. The validity of the mathematical model of the real studied wind turbine has been checked thanks to a good comparison between the simulated characteristics and the real ones. The efficiency improvement was also performed. The model can simulate the real behavior of Gamesa G52, which makes the possibility of making future studies, such as the prediction of the power production, the fault diagnostics, fault tolerant control of the wind turbine system and other tests of performance improvement. Because time is money, all these predictions should be perfectly achieved as soon as possible. Declaration of Competing Interest I declare that I have no significant competing financial, professional, or personal interests that might have influenced the performance or presentation of the work described in this manuscript. CRediT authorship contribution statement Abdeldjalil Dahbi: Conceptualization, Formal analysis, Data curation, Writing - original draft, Writing - review & editing. Abdellatif Reama: Writing - review & editing. Messaoud Hamouda: Formal analysis, Data curation. Nasreddine Nait-Said: Writing - review & editing. Mohamed-Said Nait-Said: Formal analysis. Acknowledgments The used data in this paper were provided by SKTM through the SKTM-CDER convention. The authors highly thank Mr. Laroui Rachid, head of Kaberten wind farm, as well as Mr. Boulekhrass, PDG of SKTM for their profitable collaboration. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.compeleceng. 2019.106492. References [1] Louassa S, Guerri O, Kaabeche A, Yassaa N. Effects of local ambient air temperatures on wind park performance: case of the Kaberten wind park. Energy Sources 2019;41:1556–7230 03 Oct. [2] Benmedjahed M, Maouedj R. 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[17] Guellai N, Benmedjahed M, Boudia SM, Benmansur A, Tabet Hellal AM. Evaluation du gisement éolien dans la région des hauts plateaux Algériens : (cas de Batna). In: Proceedings of the 2nd international conference on energy and sustainable development; 2013. [18] Polígono Industrial Agustinos, C/A s/n. GAMESA G52-850 kW; 2007. Report. [19] Aimani SE. Towards a practical identification of a DFIG based wind generator model for grid assessment. Energy Procedia 2012;14:1677–83. [20] Dahbi A, Nait-Said N, Nait-Said MS. .A novel combined MPPT-pitch angle control for wide range variable speed wind turbine based on neural network. Int J Hydrogen Energy 2016;41:9427–42. [21] Dahbi A, Hachemi M. Influence of the parameters variations on the power injected to the network by wind turbine using PMSG. Acta Electroteh 2013;54(1):31–44. [22] Wei C-C. Conceptual weather environmental forecasting system for identifying potential failure of under-construction structures during typhoons’. J Wind Eng Ind Aerodyn 2017;168:48–59. [23] Zhang Z-Y, Wang K-S. Wind turbine fault detection based on SCADA data analysis using ANN. Adv Manuf 2014;2:70–8. Abdeldjalil Dahbi was born in Ouargla, Algeria. He received his engineering in electromechanics from the University of Bordj Bou Arréridj, 2009, and his Ph.D. degree in electric control from the University of Batna, 2018. He is currently a researcher at URERMS. He had patents and he reviewed in journals. His main research interests are: renewable energies, power electronics, system controls. Abdellatif Reama was born in Safi, Morocco. He obtained his Ph.D. degree in electrical engineering from the National Polytechnic Institute of Toulouse (ENSEEIHT) in 1987, France. He is a professor at the Department of Systems Engineering, ESIEE-Paris. His research field covers several aspects of design and control, with the optimization and real-time systems. Messaoud Hamouda was born in Adrar, Algeria. He received his Ph.D. degree in Electrotechnic in 2007 from the University of Oran (USTO-MB), Algeria. He was the Director of URERMS Research Unit. He is also a Professor and the Director of the Sustainable Development Laboratory (SDL) at Adrar’s University. His current research interests include renewables energies, power electronics and sustainable development. Nasreddine Naït-Saïd was born in Batna, Algeria. He received the Engineer Diploma in Electronic from the National Polytechnic Algiers High School, in 1988. He received the Ph.D. degree in electrical engineering from the University of Batna in 2003, where he is currently a full professor. His research interests include the electric drives and diagnosis, artificial neural networks, and renewable energy. Mohamed-Said Naït-Saïd was born in Batna, Algeria. He received his Ph.D. degree in electrical engineering from the University of Batna, 1992. Currently he is a full professor at Batna’s University, where he headed a research laboratory (LSPIE), Master course of Control and Diagnosis, and scientific committee. His research interests include the electric machines and their control drives and diagnosis.